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require(dplyr)Carregando pacotes exigidos: dplyr
Attaching package: 㤼㸱dplyr㤼㸲
The following objects are masked from 㤼㸱package:stats㤼㸲:
filter, lag
The following objects are masked from 㤼㸱package:base㤼㸲:
intersect, setdiff, setequal, unionrequire(igraph)Carregando pacotes exigidos: igraph
Attaching package: 㤼㸱igraph㤼㸲
The following objects are masked from 㤼㸱package:dplyr㤼㸲:
as_data_frame, groups, union
The following objects are masked from 㤼㸱package:stats㤼㸲:
decompose, spectrum
The following object is masked from 㤼㸱package:base㤼㸲:
union# Load Hospitalization Data
flows<-read.csv(url('https://s3-sa-east-1.amazonaws.com/ckan.saude.gov.br/SRAG/2021/INFLUD21-05-07-2021.csv'))%>%
filter()# Load Hospitalization Data
flows<-read.table('Z:/Master/Datasus/raw/SRAG/influd21-18-06-2021.csv',sep=';',header=T,encoding='UTF-8')%>%
filter(SG_UF_INTE=='SP'&SG_UF=='SP')%>%
mutate(date=as.Date(DT_INTERNA,format='%D/%M/%Y'),
age=NU_IDADE_N)
flows_count<-flows%>%
filter(!is.na(CO_MUN_RES)&!is.na(CO_MU_INTE))%>%
group_by(mun_res=CO_MUN_RES,mun_hosp=CO_MU_INTE)%>%
summarize(count=n(),count_icu=sum(ifelse(UTI==1,1,0)))%>%
mutate(share=count/sum(count),share_icu=count_icu/sum(count_icu,na.rm=T))`summarise()` regrouping output by 'mun_res' (override with `.groups` argument)# Load Shapefile
require(geobr)Carregando pacotes exigidos: geobrrequire(sf)Carregando pacotes exigidos: sf
Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1shp<-read_municipality(year=2020)%>%
filter(abbrev_state=='SP')%>%
mutate(mun=floor(code_muni/10))%>%
select(mun,name_muni,geom)Using year 2020
Loading data for the whole country. This might take a few minutes.
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|====================================================================================================================================| 100%shp_centroid<-shp%>%
group_by(mun)%>%
mutate(centr=st_centroid(geom))
require(ggplot2)
require(plotly)
# Network
nodes<-flows_count%>%
data.table::melt(measure.vars=c('mun_res','mun_hosp'))%>%
distinct(value)%>%
dplyr::rename(mun=value)
edges<-nodes%>%rename(mun_res=mun)%>%
merge(nodes%>%rename(mun_hosp=mun),all=T)%>%
left_join(flows_count,by=c('mun_res','mun_hosp'))%>%
mutate_at(.vars=c('share','share_icu'),.funs=function(x) {ifelse(is.na(x),0,x)})%>%
rename(weight=share)
dir_net<-graph_from_data_frame(edges%>%rename(),nodes,directed=TRUE)
nodes_cluster<-nodes%>%
mutate(cluster_fg=cluster_fast_greedy(as.undirected(dir_net))$membership,
label=reorder(paste0('Cluster: ',cluster_fg),cluster_fg))
# Map
gph=ggplot(nodes_cluster%>%
inner_join(shp,by=c('mun')),aes(text=name_muni))+
geom_sf(aes(geometry=geom,fill=label),lwd=0.1,show.legend=F)+
geom_sf(data=shp_centroid%>%filter(name_muni=='São Paulo')%>%ungroup,aes(geometry=centr))+
theme_void()+
theme(axis.line=element_blank())+
scale_fill_manual(values=rep(RColorBrewer::brewer.pal(n=12,name='Paired'),times=10))+
guides(fill=guide_legend(ncol=3))+
labs(caption='Note: Check cluster by hovering over each municipality')
plotly::ggplotly(gph,tooltip=c('text','fill'))# Map
gph=ggplot(nodes_cluster%>%
inner_join(shp,by=c('mun')),aes(text=name_muni))+
geom_sf(aes(geometry=geom,fill=label),lwd=0.1,show.legend=F)+
geom_sf(data=shp_centroid%>%filter(name_muni=='São Paulo')%>%ungroup,aes(geometry=centr))+
theme_void()+
theme(axis.line=element_blank())+
scale_fill_manual(values=rep(RColorBrewer::brewer.pal(n=12,name='Paired'),times=10))+
guides(fill=guide_legend(ncol=3))+
labs(caption='Note: Check cluster by hovering over each municipality')
plotly::ggplotly(gph,tooltip=c('text','fill'))